Data Science Salaries
Predicting Data Science Salaries
Anh Nguyen, Amira Bendjama, Hong Doan
1. Introduction and Problem Statement
The field of data science has experienced remarkable growth in recent years, with organizations across diverse industries recognizing the value of data-driven decision making. According to an article by 365 Data Science, the US Bureau of Labor Statistics estimated that the employment rate for data scientists will grow by 36% from 2021 to 2031. This rate is significantly higher than the average growth rate of 5%, indicating substantial growth and demand for data science talent. The surging demand for data science presents both opportunities and challenges for job seekers, particularly recent graduates. One of the significant hurdles they face is the lack of salary transparency in the data science job market. This opacity creates uncertainty regarding compensation and hinders job seekers' ability to negotiate fair salaries.
There are significant variations in data science salaries across different industries and locations. For instance, according to Zippia, data scientists working in the finance and technology sectors tend to earn higher salaries compared to those in other industries. Similarly, the geographical location also plays a crucial role in determining salaries. Large cities with higher concentration of tech companies and living costs such as San Francisco and New York offer higher salaries than smaller cities.
The discrepancies in data science salaries can also be attributed to various factors, including job responsibilities, experience level, educational background, and specific skill sets. A study conducted by Burtch Works, a leading executive recruiting firm, found that data scientists with advanced degrees, such as Ph.D., tend to command higher salaries compared to those with bachelor's or master's degrees. Similarly, professionals with expertise in specialized areas, such as machine learning or natural language processing, often earn higher salaries due to the high demand for these skills.
According to a report surveyed 1,000 US-based full-time employees, conducted by Visier, 79% of all survey respondents want some form of pay transparency and 32% want total transparency, in which all employee salaries are publicized. However, the 2022 Pay Clarity Survey by WTW found that only 17% of companies are disclosing pay range information in U.S. locations where not required by state or local laws. For the states that have pay transparency laws such as Colorado and New York, there has been a decline in job postings since the law went into effect. Some employers comply with the new laws by expanding the salary ranges, sometimes to ridiculous lengths. These statistics highlight the lack of pay transparency not only in the field of data science, but across multiple job markets. Job seekers often struggle to estimate salaries for data science positions due to the scarcity of reliable information.
To address this problem, our project aims to develop a multiclass classification model that predict the the salary range for data science jobs. By leveraging publicly available data and employing machine learning algorithms, we seek to provide job seekers a better understanding of salary expectations within the data science job market and empower them to negotiate fair and competitive compensation packages.2. Data Sources and Data preparation
- Install packages
#install.packages("rpart.plot")
#install.packages("ggplot2")
#install.packages("e1071")
# Install the plotly package
#install.packages("plotly")- Import data
# Read the first CSV file
data1 <- read.csv("ds_salaries_2023.csv")
# Read the second CSV file excluding the first column
data2 <- read.csv("ds_salaries.csv")[,-1]
# Append rows from data2 to data1
combined_data <- rbind(data2, data1)
# Write the combined data to a new CSV file
write.csv(combined_data, "combined_salaries.csv", row.names = FALSE)library(ggplot2)
ds_salaries <- read.csv("combined_salaries.csv")- Data description
summary(ds_salaries)## work_year experience_level employment_type job_title
## Min. :2020 Length:4362 Length:4362 Length:4362
## 1st Qu.:2022 Class :character Class :character Class :character
## Median :2022 Mode :character Mode :character Mode :character
## Mean :2022
## 3rd Qu.:2023
## Max. :2023
## salary salary_currency salary_in_usd employee_residence
## Min. : 4000 Length:4362 Min. : 2859 Length:4362
## 1st Qu.: 93918 Class :character 1st Qu.: 90000 Class :character
## Median : 135000 Mode :character Median :130000 Mode :character
## Mean : 209246 Mean :134054
## 3rd Qu.: 180000 3rd Qu.:173000
## Max. :30400000 Max. :600000
## remote_ratio company_location company_size
## Min. : 0.0 Length:4362 Length:4362
## 1st Qu.: 0.0 Class :character Class :character
## Median : 50.0 Mode :character Mode :character
## Mean : 49.7
## 3rd Qu.:100.0
## Max. :100.0- The first 5 rows
head(ds_salaries,5)This dataset has 607 rows and 12 columns
We want to focus on “USD” currency so we keep the “salary_in_usd” column and drop “salary_currency” and “salary” column by using subset()
ds_salaries <- subset(ds_salaries, select = -c( salary_currency, salary))
head(ds_salaries, 5)- Check for null values
num_null_rows <- sum(rowSums(is.na(ds_salaries)) == ncol(ds_salaries))
print(num_null_rows)## [1] 0There are no null values
- Check for duplicate rows
repeated_entries <- subset(ds_salaries, duplicated(ds_salaries))
print(repeated_entries)## work_year experience_level employment_type
## 218 2021 MI FT
## 257 2021 MI FT
## 332 2022 SE FT
## 333 2022 SE FT
## 334 2022 SE FT
## 354 2022 SE FT
## 363 2022 SE FT
## 364 2022 SE FT
## 371 2022 SE FT
## 375 2022 MI FT
## 378 2022 SE FT
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## 407 2022 MI FT
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## job_title salary_in_usd employee_residence
## 218 Data Scientist 90734 DE
## 257 Data Engineer 200000 US
## 332 Data Analyst 90320 US
## 333 Data Analyst 112900 US
## 334 Data Analyst 90320 US
## 354 Data Scientist 123000 US
## 363 Data Analyst 130000 CA
## 364 Data Analyst 61300 CA
## 371 Data Scientist 123000 US
## 375 ETL Developer 54957 GR
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## 4362 50 IN LThere are 42 duplicate rows
- Remove duplicates
# Remove duplicate rows
df <- ds_salaries[!duplicated(ds_salaries), ]
# check again
repeated_entries_new <- subset(df, duplicated(df))
print(repeated_entries_new)## [1] work_year experience_level employment_type job_title
## [5] salary_in_usd employee_residence remote_ratio company_location
## [9] company_size
## <0 rows> (or 0-length row.names)Salaries groups
Adding new column to split our salaries into three groups Low , High, Medium.The approach is to use Percentiles by Dividing the dataset based on them. Hence, we are classifying salaries below the 25th percentile as “Low”, salaries between the 25th and 75th percentile as “Medium”, and salaries above the 75th percentile as “High”.
# adding new column
# Calculate the percentiles
percentiles <- quantile(df$salary_in_usd, probs = c(0.25, 0.75))
# Define the thresholds
low_threshold <- percentiles[1] # 25th percentile
high_threshold <- percentiles[2] # 75th percentile
# Create a new column based on percentiles
df$salary_classification <- ifelse(df$salary_in_usd < low_threshold, "Low",
ifelse(df$salary_in_usd > high_threshold, "High", "Medium"))
table(df$salary_classification)##
## High Low Medium
## 644 667 1357- Data Exploration and Visualization
Top 10 Jobs in the dataset:
# Get top 10 job titles and their value counts
top10_job_title <- head(sort(table(df$job_title), decreasing = TRUE), 10)
top10_job_title_df <- data.frame(job_title = names(top10_job_title), count = as.numeric(top10_job_title))
top10_job_title_df# Load the required packages
library(plotly)##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
##
## last_plot## The following object is masked from 'package:stats':
##
## filter## The following object is masked from 'package:graphics':
##
## layout# Define custom color palette
custom_colors <- c("#FF6361", "#FFA600", "#FFD700", "#FF76BC", "#69D2E7", "#6A0572", "#FF34B3", "#118AB2", "#FFFF99", "#FFC1CC")
# Create bar plot
fig <- plot_ly(data = top10_job_title_df, x = ~reorder(job_title, -count), y = ~count, type = "bar",
marker = list(color = custom_colors), text = ~count) %>%
layout(title = "Top 10 Job Titles", xaxis = list(title = "Job Titles"), yaxis = list(title = "Count"),
font = list(size = 17), template = "plotly_dark")
# Adjust layout settings to avoid label overlap
fig <- fig %>% layout(
margin = list(b = 150), # Increase bottom margin to provide space for labels
xaxis = list(
tickangle = 45, # Rotate x-axis tick labels
automargin = TRUE # Automatically adjust margins to avoid overlap
)
)
# Display the plot
fig- Data engineer, data scientist and data analyst are the top 3 job titles in the data set. 74% of our data fall under these job title categories.The distribution of this job title category is imbalanced.
Experience level categories:
Our Dataset has 4 different experience categories: - EN: Entry-level / Junior - MI: Mid-level / Intermediate - SE: Senior-level / Expert - EX: Executive-level / Director
# Create a mapping of category abbreviations to full names
category_names_experience <- c("EN" = "Entry-level",
"MI" = "Mid-level",
"SE" = "Senior-level",
"EX" = "Executive-level")
# Get the sorted experience data
experience <- head(sort(table(df$experience_level), decreasing = TRUE))
# Replace the category names with full forms
names(experience) <- category_names_experience[names(experience)]
# Calculate the percentage for each category
percentages <- round(100 * experience / sum(experience), 2)
# Define a custom color palette
custom_colors <- c("#FFA998", "#FF76BC", "#69D2E7", "#FFA600")
# Create a pie chart with cute appearance
pie(experience, labels = paste(names(experience), "(", percentages, "%)"), col = custom_colors, border = "white", clockwise = TRUE, init.angle = 90)
# Add a legend with cute colors
legend("topright", legend = names(experience), fill = custom_colors, border = "white", cex = 0.8)
# Add a title with a cute font
title("Experience Distribution", font.main = 1)
- Senior-level category accounts for almost 59% of our data, followed by
mid-level (27%) and entry-level (10%). The distribution of experience
category is imbalanced.
Compnay size distribution
# Create a mapping of category abbreviations to full names
category_names_company <- c("M" = "Medium",
"L" = "Large",
"S" = "Small"
)
# Get the sorted company size data
company_size <- head(sort(table(df$company_size), decreasing = TRUE))
# Replace the category names with full forms
names(company_size) <- category_names_company[names(company_size)]
# Set the maximum value for the y-axis
max_count <- max(company_size)
# Create a bar plot with adjusted y-axis limits
barplot(company_size, col = custom_colors, main = "Company Size Distribution", xlab = "Company Size", ylab = "Count", ylim = c(0, max_count + 10))
- The company size category distribution is imbalanced with majority of
the data falls under medium size.
Salaries Distribution
# Set the scipen option to a high value
options(scipen = 10)
# Create boxplot of salaries
bp <- boxplot(df$salary_in_usd / 1000,
col = "skyblue",
main = "Boxplot of Salaries",
ylab = "Salary in Thousands USD",
notch = TRUE)
- For the salary attribute, the median value is a little above $100,000.
The min value is around $70,000. The max value is around $300,000. There
are some outlines, which show salary greater than the max value. These
could be the salary of the executives.
Salaries classification Distribution
# Get the sorted salary classification data
salary_classification <- sort(table(df$salary_classification), decreasing = TRUE)
salary_classification_df <- data.frame(salary_classification= names(salary_classification ), count = as.numeric(salary_classification ))
fig <- plot_ly(
data = salary_classification_df,
x = ~reorder(salary_classification, -count),
y = ~count,
type = "bar",
marker = list(color = custom_colors),
text = ~count,
width = 700,
height = 400
)
fig <- fig %>% layout(
title = "Salary Classification Distribution",
xaxis = list(title = "Salary Classification"),
yaxis = list(title = "Count"),
font = list(size = 17),
template = "ggplot2"
)
fig- For salary range category, the medium range accounts for approximatly half of our data.
# Create a data frame with counts of experience levels by salary classification
experience_salary <- table(df$experience_level, df$salary_classification)
# Define custom colors for each experience level
custom_colors <- c("#69D2E7", "#1900ff", "#FF6361", "#FFD700")
# Create a data frame for the plot
plot_data <- data.frame(Experience = rownames(experience_salary),
Salary_Classification = colnames(experience_salary),
Count = as.vector(experience_salary))
# Convert Count column to numeric
plot_data$Count <- as.numeric(plot_data$Count)
# Create the bar plot
library(plotly)
fig <- plot_ly(data = plot_data, x = ~Salary_Classification, y = ~Count,
color = ~Experience, colors = custom_colors, type = "bar") %>%
layout(title = "Experience Level by Salary Classification",
xaxis = list(title = "Salary Classification"),
yaxis = list(title = "Count"),
font = list(size = 17),
template = "plotly_dark")
fig<<<<<<< HEAD ## 4. Modeling ======= 4. Modeling a. Logistic regression
#install.packages("nnet")# Calculate the percentiles
percentiles <- quantile(df$salary_in_usd, probs = c(0.5))
# Define the threshold
threshold <- percentiles[1] # 50th percentile
# Create a new column based on the threshold
df$salary_classification_Binary <- ifelse(df$salary_in_usd < threshold, "Low", "High")
# Display the table of salary classifications
table(df$salary_classification_Binary)##
## High Low
## 1334 1334# Calculate the median of the column df$salary_in_usd
median_salary <- median(df$salary_in_usd, na.rm = TRUE)
print(median_salary)## [1] 127344a. Logistic regression
df$company_location <- ifelse(df$company_location == "US", "US", "Other")
df$employee_residence <- ifelse(df$employee_residence == "US", "US", "Other")
df$job_title <- ifelse(grepl("Data Science", df$job_title) | grepl("Data Scientist", df$job_title), "Data Scientist",
ifelse(grepl("Analyst", df$job_title) | grepl("Analytics", df$job_title), "Data Analyst",
ifelse(grepl("Data Engineer", df$job_title) | grepl("Data Engineering", df$job_title), "Data Engineer",
"Other")))table(df$job_title)##
## Data Analyst Data Engineer Data Scientist Other
## 598 659 697 714table(df$employee_residence)##
## Other US
## 768 1900table(df$company_location)##
## Other US
## 732 1936df <- data.frame(lapply(df, factor))
factors <- sapply(df, is.factor)
factor_cols <- names(df[factors])
factor_cols## [1] "work_year" "experience_level"
## [3] "employment_type" "job_title"
## [5] "salary_in_usd" "employee_residence"
## [7] "remote_ratio" "company_location"
## [9] "company_size" "salary_classification"
## [11] "salary_classification_Binary"# 3 - 58
set.seed(3) # Set a seed for reproducibility
train_indices <- sample(1:nrow(df), 0.9 * nrow(df)) # 80% for training
train_data <- df[train_indices, ]
test_data <- df[-train_indices, ]
# Separate the features (independent variables) from the target variable
X <- train_data[, !(names(train_data) %in% c("salary_in_usd", "salary_classification"))]
#X <- train_data[,c("experience_level","company_size","remote_ratio")]
Y <- train_data$salary_classificationlibrary(nnet)
# Fit the logistic regression model
logistic_model <- multinom(Y ~ ., data = X)## # weights: 63 (40 variable)
## initial value 2637.768105
## iter 10 value 1688.858164
## iter 20 value 1519.556578
## iter 30 value 1447.811556
## iter 40 value 1421.162829
## iter 50 value 1419.866694
## final value 1419.860084
## converged# Make predictions on the test data
test_data$predicted_classification <- predict(logistic_model, newdata = test_data)
# Evaluate model performance
library(caret)## Loading required package: latticeconfusion_matrix <- confusionMatrix(test_data$predicted_classification, test_data$salary_classification)
print(confusion_matrix)## Confusion Matrix and Statistics
##
## Reference
## Prediction High Low Medium
## High 36 0 24
## Low 0 52 18
## Medium 25 10 102
##
## Overall Statistics
##
## Accuracy : 0.7116
## 95% CI : (0.6533, 0.7652)
## No Information Rate : 0.5393
## P-Value [Acc > NIR] : 0.000000006182
##
## Kappa : 0.528
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: High Class: Low Class: Medium
## Sensitivity 0.5902 0.8387 0.7083
## Specificity 0.8835 0.9122 0.7154
## Pos Pred Value 0.6000 0.7429 0.7445
## Neg Pred Value 0.8792 0.9492 0.6769
## Prevalence 0.2285 0.2322 0.5393
## Detection Rate 0.1348 0.1948 0.3820
## Detection Prevalence 0.2247 0.2622 0.5131
## Balanced Accuracy 0.7368 0.8755 0.7119b. Random Forest
# Load the random Forest package
library(randomForest)## randomForest 4.7-1.1## Type rfNews() to see new features/changes/bug fixes.##
## Attaching package: 'randomForest'## The following object is masked from 'package:ggplot2':
##
## marginlibrary(caret)
# Train the Random Forest classifier
rf_model <- randomForest(X, Y)
# Make predictions on new data
# Assuming you have a data frame called test_data with similar features as train_data
predictions <- predict(rf_model, test_data)
# Calculate accuracy
accuracy <- sum(predictions == test_data$salary_classification) / length(test_data$salary_classification)
cat("Accuracy:", accuracy, "\n")## Accuracy: 0.6966292# Create confusion matrix
conf_matrix <- table(predictions, test_data$salary_classification)
cat("Confusion Matrix:\n")## Confusion Matrix:print(conf_matrix)##
## predictions High Low Medium
## High 35 0 26
## Low 0 50 17
## Medium 26 12 101# Calculate precision, recall, and F1-score for each class
class_metrics <- caret::confusionMatrix(predictions, test_data$salary_classification)
cat("Class Metrics:\n")## Class Metrics:print(class_metrics$byClass)## Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: High 0.5737705 0.8737864 0.5737705 0.8737864 0.5737705
## Class: Low 0.8064516 0.9170732 0.7462687 0.9400000 0.7462687
## Class: Medium 0.7013889 0.6910569 0.7266187 0.6640625 0.7266187
## Recall F1 Prevalence Detection Rate
## Class: High 0.5737705 0.5737705 0.2284644 0.1310861
## Class: Low 0.8064516 0.7751938 0.2322097 0.1872659
## Class: Medium 0.7013889 0.7137809 0.5393258 0.3782772
## Detection Prevalence Balanced Accuracy
## Class: High 0.2284644 0.7237784
## Class: Low 0.2509363 0.8617624
## Class: Medium 0.5205993 0.6962229importance <- varImp(rf_model)
print(importance)## Overall
## work_year 34.866046
## experience_level 89.048584
## employment_type 7.855774
## job_title 59.381201
## employee_residence 108.449621
## remote_ratio 31.218494
## company_location 74.606604
## company_size 30.410023
## salary_classification_Binary 224.359158c. Support Vector Machine (SVM)
library(e1071)
# Train the SVM classifier
svm_model <- svm(Y ~ ., data = X, kernel = "radial")
# Make predictions on new data
# Assuming you have a data frame called test_data with similar features as train_data
predictions <- predict(svm_model, test_data)
# Evaluate the model
# Assuming you have the actual target variable values in test_data$salary_classification
accuracy <- sum(predictions == test_data$salary_classification) / length(test_data$salary_classification)
cat("Accuracy:", accuracy, "\n")## Accuracy: 0.6779026# Create confusion matrix
conf_matrix <- table(predictions, test_data$salary_classification)
cat("Confusion Matrix:\n")## Confusion Matrix:print(conf_matrix)##
## predictions High Low Medium
## High 24 0 18
## Low 0 48 17
## Medium 37 14 109d. Decision Tree
library("rpart")
library("rpart.plot")
decision_tree <- rpart(Y ~ .,
data = X,
method="class")
# I only tried attributes with a limited number of unique values because using attributes like job_title and employee_residence caused the program to run endlessly.
# remote_ratio is the most useful variable for prediction
# Make predictions on test data
predictions <- predict(decision_tree, newdata = test_data, type = "class")
# Evaluate the model
accuracy <- sum(predictions == test_data$salary_classification) / nrow(test_data)
print(paste("Accuracy:", accuracy))## [1] "Accuracy: 0.700374531835206"rpart.plot(decision_tree)
Major Challenges and Solutions
- Data is not updated
- Dataset imbalance
- Data is imbalanced
Conclusion and Future Work
References
The Data Scientist Job Outlook in 2023 | 365 Data Science
Burtch-Works-Study_DS-PAP-2019.pdf (burtchworks.com)
New Visier Report Reveals 79% of Employees Want Pay Transparency (prnewswire.com)
More NA organizations plan to disclose pay information - WTW (wtwco.com)